Time-saving impact of an algorithm to identify potential surgical site infections

Infect Control Hosp Epidemiol. 2013 Oct;34(10):1094-8. doi: 10.1086/673154. Epub 2013 Aug 29.


Objective: To develop and validate a partially automated algorithm to identify surgical site infections (SSIs) using commonly available electronic data to reduce manual chart review.

Design: Retrospective cohort study of patients undergoing specific surgical procedures over a 4-year period from 2007 through 2010 (algorithm development cohort) or over a 3-month period from January 2011 through March 2011 (algorithm validation cohort).

Setting: A single academic safety-net hospital in a major metropolitan area.

Patients: Patients undergoing at least 1 included surgical procedure during the study period.

Methods: Procedures were identified in the National Healthcare Safety Network; SSIs were identified by manual chart review. Commonly available electronic data, including microbiologic, laboratory, and administrative data, were identified via a clinical data warehouse. Algorithms using combinations of these electronic variables were constructed and assessed for their ability to identify SSIs and reduce chart review.

Results: The most efficient algorithm identified in the development cohort combined microbiologic data with postoperative procedure and diagnosis codes. This algorithm resulted in 100% sensitivity and 85% specificity. Time savings from the algorithm was almost 600 person-hours of chart review. The algorithm demonstrated similar sensitivity on application to the validation cohort.

Conclusions: A partially automated algorithm to identify potential SSIs was highly sensitive and dramatically reduced the amount of manual chart review required of infection control personnel during SSI surveillance.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't
  • Validation Study

MeSH terms

  • Algorithms*
  • Automation
  • Humans
  • Infection Control / statistics & numerical data*
  • Population Surveillance / methods*
  • Retrospective Studies
  • Sensitivity and Specificity
  • Surgical Wound Infection / diagnosis*
  • Time Factors
  • Workload